1. Field of the Invention
Methods and apparatuses consistent with the present invention relate to adaptive false contour reduction, and more particularly, to adaptive false contour reduction which is capable of removing a false-contour region by detecting the false-contour region of an input image and smoothing only the false-contour region.
2. Description of the Related Art
As display devices are increasing in size in the digital television (DTV) era, an artifacts problem rises which did not appear in the past. That is, there is no artifacts problem when images are displayed on a cathode ray tube (CRT) monitor or a small-sized TV, but various artifacts problems have come up with larger and wider TV screens in the DTV era. In order to obtain much clearer images, images input in digital data are image-processed by an enhancement process such as Contrast Enhancement (CE), Detail Enhancement (DE), and the like, but there has emerged a side effect of artifacts that was not taken into account in the past.
Artifacts refer to unnatural components on images, which contains diverse kinds of noise occurring due to limits to the Charge Coupled Device (CCD) sensor, mosquito noise, dynamic false contours due to characteristics of display devices, ghosts occurring due to problems in transmission channels, and so on. The false contours are typical of such artifacts. The false contours refer to contour-shaped artifacts appearing on a flat region of an image such as sky, water surface, skin, or the like. The flat region can be referred to as a region of which brightness is gradually changing rather than a region of which pixels have the exact same values. If such a flat region has some distinct brightness values to an extent offensive to eyes, the flat region has parts appearing like contour lines. The eye-offensive contour lines in the flat region are referred to as false contours or false edges, which are differentiated from edges being a signal component of an image.
The false contour occurs due to various causes, but, in general, mainly occur at the time the quantization level for a brightness value is not enough. The quantization level determining a brightness value is decided based on the number of bits (a bit depth) expressing a digitized brightness value. The false contour does not appear to an extent of the existing bit depth, but appears on the scaled-up display devices. Further, the false contour appears at the time of the CE or the DE process and even at the time of compressions or decompressions of images into or from Joint Photographic Experts Groups (JPEG) or Moving Picture Experts Groups (MPEG). The conventional false contour removal methods include the blue noise mask method, dithering method, Daly and Feng method, and so on.
FIG. 1 is a view for explaining the conventional Daly and Feng method for removing a false contour. The bit depth of an input image is P, and the bit depth of an input image through a low-pass filter 10 is R.
In the conventional false contour removal method, an image having the bit depth of P is input to the low-pass filter 10 which smoothes the image by adding adjacent pixels to the pixels of the input image. The input image passing through the low-pass filter 10 increases its bit depth since the adjacent pixels are added to the pixels of the image. Thus, the bit depth R of an input image passed through the low-pass filter 10 becomes higher than the bit depth P of an input image.
The quantization unit 20 re-quantizes the pixel values of the input image having the bit depth increased through the low-pass filter 10.
Further, the first adder 30 outputs a difference value between an output value of the low-pass filter 10 and an output value of the quantization unit 20.
The second adder 40 outputs a difference value between an output value of the first adder 30 and the original image. That is, the second adder 40 adds to the original image a difference between a re-quantized pixel value and an original pixel value. Thus, the brightness value of the input image is gradually changed so that a false contour disappears since the second adder 40 adds to the original image a difference value between a value of the original image and the re-quantized value.
However, the conventional Daly and Feng false contour removal method is applied to all pixels of an input image. Thus, the method has a problem that an output image has edges or texture corresponding to signal components which are degraded to become blurry since the entire input image passes through the low-pass filter. Further, the method has a problem since it can be applied only with limited conditions assuming that the bit depth of an input image is lower than the bit depth of an output image or knowing an original image prior to false contour occurrence due to quantization.
Further, the conventional false contour removal method can be properly applied when the causes of the false contour are known, and has limitations that false contour can not be precisely determined. In particular, the Daly and Feng method can not be used as a proper false contour removal method at the time a difference between a value obtained through a low-pass filter and a re-quantized value is not enough for false contour removal.
Accordingly, it is necessary to first detect a false contour from an input image and adaptively remove only the detected false contour.